AI and ML Infrastructure

What is AI and ML Infrastructure?

AI and ML infrastructure refers to the underlying technology and systems that allow for the creation, deployment, and management of artificial intelligence (AI) and machine learning (ML) models. This can include data storage and processing systems, computational resources, and software frameworks, among other things.

What will I learn in these lectures?

In these lectures, you'll gain a comprehensive introduction to Python, one of the most widely used programming languages in the AI and ML field. You will learn the basics of Python programming, including variables, loops, functions, and error handling.

Beyond Python, we'll introduce you to NumPy and scikit-learn, essential libraries for scientific computing in Python. You'll understand the basics of array manipulation with NumPy and get started with data analysis and machine learning using scikit-learn.

We'll also provide an overview of various AI and ML infra packages. These include TensorFlow and PyTorch, which are powerful tools for creating and training complex machine learning models. You'll get a taste of building, training, and deploying a machine learning model, paving the way for more advanced understanding of AI and ML infrastructure.

I already know about Python and some of the packages you've mentioned. Is this still beneficial?

Absolutely! We'll be matching you according to your proficiency and knowledge competency. Prior to the bootcamp, we conduct assessments to gauge the programming competency of each participant. This enables us to place you in the correct cohort where the pace and depth of the curriculum align with your skills and knowledge. If you're advanced, you'll be with peers who are at a similar level, ensuring that the content and pace are challenging and stimulating.

During these lectures, we have material prepared for all skill levels -- you'll get the chance to delve deeper into topics such as complex array manipulations with NumPy, advanced machine learning algorithms in scikit-learn, or optimization techniques in TensorFlow and PyTorch. The course content will be adjusted to ensure that you are continually learning and advancing your skills.

Moreover, a refresher can help you get more rigorous practice, challenge your understanding, and allow you to gain new perspectives. By revisiting these topics, you can solidify your mastery, making it easier to learn more complex topics in AI and ML infrastructure.